---
license: apache-2.0
language:
- ru
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- asr
- Pytorch
- pruned
- audio
- automatic-speech-recognition
metrics:
- cer
- wer
model-index:
- name: Whisper Small Pruned for Russian
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15.0 (Russian part, test)
      type: mozilla-foundation/common_voice_15_0
      args: ru
    metrics:
    - name: WER
      type: wer
      value: 24.98
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 15.0 (Russian part, test)
      type: mozilla-foundation/common_voice_15_0
      args: ru
    metrics:
    - name: WER (without punctuation)
      type: wer
      value: 17.48
---

# Whisper-small-ru-pruned

## Model info
This is a pruned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) model with only russian tokens left.
Pruning was made without any fine-tuning. Method from [this post](https://medium.com/m/global-identity-2?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) was used.

## Size
Only 10% tokens was left including special whisper tokens (no language tokens except \<|ru|\> and \<|en|\>, no timestamp tokens), 200 most popular tokens from tokenizer and 4000 most popular Russian tokens computed by tokenization of russian text corpus.

Model size is 15%  less then original whisper-small:
|  | openai/whisper-small | waveletdeboshir/whisper-small-ru-pruned |
| :------ | :------ | :------ |
| n of parameters | 242 M | 205 M |
| n of parameters (with proj_out layer) | 281 M | 208 M |
| model file size | 967 Mb | 834 Mb |
| vocab_size | 51865 | 4207 |

## Usage
Model can be used as an original whisper:

```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> import torchaudio

>>> # load audio
>>> wav, sr = torchaudio.load("audio.wav")

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-small-ru-pruned")
>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-small-ru-pruned")

>>> input_features = processor(wav[0], sampling_rate=sr, return_tensors="pt").input_features 

>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
['<|startoftranscript|><|ru|><|transcribe|><|notimestamps|> Начинаем работу.<|endoftext|>']

```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.

## Other pruned whisper models
* [waveletdeboshir/whisper-tiny-ru-pruned](https://huggingface.co./waveletdeboshir/whisper-tiny-ru-pruned)
* [waveletdeboshir/whisper-base-ru-pruned](https://huggingface.co./waveletdeboshir/whisper-base-ru-pruned)

## Metrics
| metric | dataset | openai/whisper-small | waveletdeboshir/whisper-small-ru-pruned |
| :------ | :------ | :------ | :------ |
| WER* | golos-test-crowd | 0.3358 | 0.3471 |
| CER* | golos-test-crowd | 0.1561 | 0.1444 |
| WER* | common_voice_15_0_test | 0.1749 | 0.1748 |
| WER | common_voice_15_0_test | 0.2492 | 0.2498 |
*Metrics were computed after text normalization

You can fine-tune this model on your data to achive better performance.

## Colab for vocab pruning
Check https://github.com/waveletdeboshir/whisper-lang-remover